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Advances in Meteorology
Volume 2017, Article ID 8601296, 15 pages
Research Article

A Quality Control Method Based on an Improved Random Forest Algorithm for Surface Air Temperature Observations

1School of Information and Control, Nanjing University of Information Science and Technology, Nanjing 210044, China
2Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China

Correspondence should be addressed to Xing Yang; moc.liamg@xgnayyrrah

Received 14 March 2017; Accepted 6 June 2017; Published 10 July 2017

Academic Editor: Peng Yu

Copyright © 2017 Xiaoling Ye et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A spatial quality control method, ARF, is proposed. The ARF method incorporates the optimization ability of the artificial fish swarm algorithm and the random forest regression function to provide quality control for multiple surface air temperature stations. Surface air temperature observations were recorded at stations in mountainous and plain regions and at neighboring stations to test the performance of the method. Observations from 2005 to 2013 were used as a training set, and observations from 2014 were used as a testing set. The results indicate that the ARF method is able to identify inaccurate observations; and it has a higher rate of detection, lower rate of change for the quality control parameters, and fewer type I errors than traditional methods. Notably, the ARF method yielded low performance indexes in areas with complex terrain, where traditional methods were considerably less effective. In addition, for stations near the ocean without sufficient neighboring stations, different neighboring stations were used to test the different methods. Whereas the traditional methods were affected by station distribution, the ARF method exhibited fewer errors and higher stability. Thus, the method is able to effectively reduce the effects of geographical factors on spatial quality control.